What is new?
The comprehension on how an ANOVA test can replace the t-test is new. After learning that a t-test could provide accurate results, it is interesting to note that the ANOVA can be used in place of the t-test in situations where distributions in a group are overlapping (Sardana, Choudhury, & Chaudhary, 2017 ) . Therefore, the averages vary within the groups making the use of a t-test unreliable as several t-tests will have to be conducted. The challenge posed by the multiple tests is the reliability of the results as the confidence in the results is reduced. Hence, with an alpha of 0.05, the ANOVA provides a confidence of 95 per cent in rejecting the null hypothesis.
Is there anything you found to be unclear?
The replacement of the application of the ANOVA with the chi-square test was unclear. An example is presented of the manufacturing industry that generates parts that differ in key metrics. The application of the chi-square test in such an example is unclear as it is not apparent whether the test can be performed on both normal and non-normal distribution data. Hence, this lack of clarity poses a challenge in the substitution of the ANOVA and the chi-square test in their statistical application.
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Correlation of Issues and Problems
A relationship is evident to be in existence between the t-test and the ANOVA as they both can perform analyze variances as well as the means of groups. The difference between the two statistical tools is noticeable in the confidence level of the results obtained from group distributions that are not overlapping and the means are varied (Sardana et al., 2017 ) . Another correlation is obvious in the application of the null hypothesis in both the ANOVA and the chi-square test.
References
Sardana, M., Choudhury, T., & Chaudhary, D. K. (2017, March). Extensive review on software testing and pipeline testing softwares. In 2017 International Conference on Big Data Analytics and Computational Intelligence (ICBDAC) (pp. 246-251). IEEE.
